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The phenolphthalein solution used in the assay of P-Cyclodextrin is not stable and undergoes spontaneous decolorization. There is linear decrease in absorbance of phenolphthalein with time. This causes a continuous increase in error with time which is as high as 80% after one hour. A simple procedure is described in the paper to circumvent this problem allowing reproducible and reliable results to be obtained. A single reagent assay was developed by incorporating phenolphthalein in Na,C03 solution. This resulted in stable absorbance readings and increased the linearity of assay up to 200pg/ml of P-cyclodextrin. Modifizierungen der Phenolphthalein-Methode zur spektrophotometrischen Bestimmung von Beta-Cyclodextrin. Die zur Bestimmung von P-Cyclodextrin venvendete Phenolphthaleinlosung ist nicht stabil, sondern unterliegt spontaner EntWrbung. Es gibt eine lineare Verringerung der Absorption mit der Zeit. Diese bedingt eine kontinuierliche Erhohung des Fehlers mit der Zeit, die nach einer Stunde bei 80% liegen kann. Es wird ein einfaches Verfahren beschrieben, urn dieses Problem zu umgehen und reproduzierbare und zuverlassige Ergebnisse zu erhalten. Eine einzelne Reagenzanalyse wurde entwickelt unter Einbeziehung von Phenolphthalein in Na3C03Losung. Diese resultierte in stabilen Absorptionswerten und erhohte die Linearitat der Analyse bis zu 200pg/ml P-Cyclodextrin.

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This paper entails a comprehensive study on production of a biosurfactant from Rhodococcus erythropolis MTCC 2794. Two optimization techniques--(1) artificial neural network (ANN) coupled with genetic algorithm (GA) and (2) response surface methodology (RSM)--were used for media optimization in order to enhance the biosurfactant yield by Rhodococcus erythropolis MTCC 2794. ANN and RSM models were developed, incorporating the quantity of four medium components (sucrose, yeast extract, meat peptone, and toluene) as independent input variables and biosurfactant yield [calculated in terms of percent emulsification index (% EI(24))] as output variable. ANN-GA and RSM were compared for their predictive and generalization ability using a separate data set of 16 experiments, for which the average quadratic errors were approximately 3 and approximately 6%, respectively. ANN-GA was found to be more accurate and consistent in predicting optimized conditions and maximum yield than RSM. For the ANN-GA model, the values of correlation coefficient and average quadratic error were approximately 0.99 and approximately 3%, respectively. It was also shown that ANN-based models could be used accurately for sensitivity analysis. ANN-GA-optimized media gave about a 3.5-fold enhancement in biosurfactant yield.